3 papers
cs.AI2026
Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls
Jiancu Chen, Shuyin Xia, Guan Wang +2
Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting importan…
cs.LG2026
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Guan Wang, Shuyin Xia, Lei Qian +4
Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still face…
cs.LG2023
GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems
Shuyin Xia, Xinyu Lin, Guan Wang +4
Optimization problems aim to find the optimal solution, which is becoming increasingly complex and difficult to solve. Traditional evolutionary optimization methods always overlook…